The impact of lecture capture availability on academic performance in a large biomedical science course
Bibliographic record
Abstract
Abstract Lecture capture is a technology where live lectures are recorded in a digital format and made available to students to view at their convenience. The use of this technology in higher education has steadily increased despite mixed results as to whether it is beneficial to student achievement. The current study utilized a two-group quasi-experimental design to examine the impact of lecture capture availability on academic performance in a large enrollment, two-term, second year biomedical science course. Academic performance was compared between two matched cohorts enrolled in the same biomedical science course taught by the same instructor in which one course did not have access to lecture recordings (2017–18 academic year, N = 433) and the other did (2018–19 academic year, N = 414). Academic performance was evaluated by comparing scores on identical exam questions and the final grade earned in the course. Student’s t-test revealed that lecture capture availability resulted in a decline in performance on exams and the final course grade. We also evaluated whether lecture capture influenced student attendance via an in-class student response system and a t-test found that student attendance was comparable between the cohorts. A chi-squared test also found that lecture capture availability resulted in significantly more course failures. Importantly, a student’s t-test showed that GPA did not differ between the cohorts. To our knowledge this is the first study to show that lecture capture availability resulted in a decline in academic performance despite similar in-class attendance and GPA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".